Investment Casting of Near-Net Shape Gamma Titanium Aluminide Automotive Turbocharger Rotor

Kim, Myoung Gyun; Sung, Si Young; Lee, GyuChang; Park, Joon Pyo; Kim, Young Jig

doi:10.4028/0-87849-960-1.2547

Cited by 1 publication

(8 citation statements)

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“…''Process design'' papers aim to establish, [8][9][10][11]14,20,54,57,68 optimize 12,22,34,44 or enhance 18,43,49 process capabilities in terms of technological quality, 8,11,18,43,44,59,68 geometric capabilities, 9,10,14,20,54 workable material 12,34,57 or waste reduction. Investigations are mainly empirical (experimental and case studies) and analytical.…”

Section: Process Designmentioning

confidence: 99%

“…Analytical analysis on material optimization has also been conducted for powder technologies. [8][9][10][11][12]14 Much of the work on numerical analysis enhance and optimize the quality of casting, particularly investment casting, 18,22 and also defining the component shapes achievable by forging. 20 Product design ''Product design'' papers aim to evaluate, modify and establish the influence of product design on process performances, 19,29 feasibility, 25,26,28 design 15,28,30 and final product quality.…”

Section: Process Designmentioning

confidence: 99%

“…''Material characterization'' papers define metal properties in connection with a new process 62,74 (e.g. LMIM, and laser hot wire process, ALM process) or existing process 21,22,41,46,49,51,56,73 or products. Microstructure, 41,46,49,51,56,62,73,74 mechanical properties, 51,56,62,73 plastic flow/behavior 21 and other material processing parameters (e.g.…”

Section: Materials Characterizationmentioning

confidence: 99%

“…Overview of the NNS research: process innovation, process design, product design, material characterization, differential analysis, applied framework. Groenbaek and Birker, 76 Lewis et al, 52 Milewski et al, 53 Klug et al, 80 Behrens et al, 81 Vilotić et al, 82 Dean, 38 and Shi et al 61 Li, 18 Hirt et al, 68 Kim et al, 22 Chitkara and Bhutta, 8 Chitkara and Kim, 10 Chitkara and Bhutta, 9 Chitkara and Kim, 11 Netto et al, 12 Jeon and Kim, 14 Siegert et al, 34 Kapranos et al, 44 Kang et al, 43 Qi et al, 54 Taminger and Hafley, 57 Dirba et al 59 and Mamalis et al 20 Takemasu et al, 19 Tomov and Gagov, 15 Chu et al, 25 De Sam Lazaro et al, 26 Yin et al, 28 Konak et al 29 and Medellin et al 30 Blackwell and Wisbey, 73 Kottman et al, 74 Okada et al, 21 Yamamoto et al, 41 Curle, 46 Kim et al, 22 Arribas et al, 49 Qi et al, 51 Krishna et al 56 and Julien and Després 62 Tateno, 3 Bhatkal and…”

Section: Materials Characterizationmentioning

confidence: 99%

“…The aim is to characterize the Al-Al 3 Ni flow and deformation in semi-solid state forging. Kim et al 22 investigate numerically a centrifugal casting investment process (or centrifuging casting) of turbocharger rotors (TiAl alloy). Simulation provides information about mold filling, which correlate well with experimental trials and can be used to resolve production problems such as the incomplete filling of dies.…”

Section: Review Synthesismentioning

confidence: 99%

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Near net shape manufacturing of metal: A review of approaches and their evolutions

Marini

Cunningham

Corney

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

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In the last thirty years the concept of manufacturability has been applied to many different processes in numerous industries. This has resulted in the emergence of several different "Design for Manufacturing" methodologies which have in common the aim of reducing productions costs through the application of general manufacturing rules. Near net shape technologies have expanded these concepts, targeting mainly primary shaping process, such as casting or forging. The desired outcomes of manufacturability analysis for near-net-shape (NNS) processes are cost and lead/time reduction through minimization of process steps (in particular cutting and finishing operations) and raw material saving. Product quality improvement, variability reduction and component design functionality enhancement are also achievable through NNS optimization. Process parameters, product design and material selection are the changing variables in a manufacturing chain that interact in complex, non-linear ways. Consequently modeling and simulation play important roles in the investigation of alternative approaches. However defining the manufacturing capability of different processes is also a "moving target" because the various NNS technologies are constantly improving and evolving so there is challenge in accurately reflecting their requirements and capabilities. In the last decade, for example, CAD, CNC technologies and innovation in materials have impacted enormously on the development of NNS technologies. This paper reviews the different methods reported for NNS manufacturability assessment and examines how they can make an impact on cost, quality and process variability in the context of a specific production volume. The discussion identifies a lack of structured approaches, poor connection with process optimization methodologies and a lack of empirical models as gaps in the reported approaches.

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